{"id":9453,"date":"2024-06-25T16:06:18","date_gmt":"2024-06-25T14:06:18","guid":{"rendered":"https:\/\/www.fact-finder.com\/blog\/?p=9453"},"modified":"2025-05-14T15:55:49","modified_gmt":"2025-05-14T13:55:49","slug":"factfinder-response-times","status":"publish","type":"post","link":"\/blog\/factfinder-response-times\/","title":{"rendered":"FactFinder’s response times set new industry benchmarks"},"content":{"rendered":"\n

We tested our search with complex configurations, massive product datasets and tens of thousands of searches per hour. The result? Response times faster than the blink of an eye.<\/em><\/p>\n\n\n\n\n\n\n\n

There is a high cost to low performance in any customer-facing system. How efficient and effective a system behaves affects the perception of the overall quality and determines whether the user will stay or leave. In eCommerce, visitors’ first glimpse of your system performance is often the search<\/a>.\u00a0\u00a0<\/p>\n\n\n\n

Your search performance can ensure the staying power of your online shop in a highly competitive market or shorten its lifespan. So, it\u2019s no surprise that online retailers spend countless hours and resources optimizing it. <\/p>\n\n\n\n

This blog will cover eCommerce search performance in-depth, provide realistic benchmarks based on unique business requirements and the performance you can achieve with our solution. <\/p>\n\n\n\n

Expect to learn:<\/p>\n\n\n\n

\u26a1 How we test response times<\/p>\n\n\n\n

\u26a1 FactFinder\u2019s response times for simple to complex shops<\/p>\n\n\n\n

\u26a1 When high speed \u2260 high performance<\/p>\n\n\n\n

\u26a1 Factors that affect search performance <\/p>\n\n\n\n

How we test response times <\/h2>\n\n\n\n

Let\u2019s start by debunking \u201caverage response time\u201d as a reliable metric. It oversimplifies the issue and hides the true performance of larger, more complex systems within the average of many smaller, simpler systems.  <\/p>\n\n\n\n

When evaluating search solutions<\/a>, decision-makers want to ensure that a new solution will match or exceed the capabilities of their current one. At the same time, they want ten additional capabilities they currently do not have. They expect to maintain the current performance and response time while adding these new features.<\/p>\n\n\n\n

Imagine driving a sports car and suddenly wanting it to transport five passengers and five pieces of luggage without sacrificing its top speed. That’s the kind of challenge we’re addressing. <\/p>\n\n\n\n

To provide useful benchmarks, we categorized customers into different clusters based on their needs and system complexities. This approach helps us give businesses realistic expectations for performance. <\/p>\n\n\n\n

For our tests, we used an internally developed tool called Go Traffic. It’s designed for flow testing and generating high traffic loads on the system. The tool sends random search terms to FactFinder, which then responds with the corresponding search results. <\/p>\n\n\n\n

We ran multiple test scenarios to understand how different configurations and product data sizes impact response time and how the system handles tens of thousands of searches per hour. We specifically measured the time between making a request and receiving a response \u2013 so, the processing time for the search system. <\/p>\n\n\n\n

One key point to highlight is that in our tests, we weren’t able to fully leverage the caching options that FactFinder offers. The caching in our system is useful for frequently used search terms. It recognizes these recurring terms and reuses previous search results, delivering quicker performance. While the simpler tests did use our caching, we can no longer use caching once we switch on capabilities such as personalization<\/a>. This means that in real-life scenarios, our solution could perform even faster than in our tests.  <\/p>\n\n\n\n

FactFinder\u2019s response times<\/h2>\n\n\n\n

We measure response times in milliseconds (ms), where one millisecond is one-thousandth of a second. To put it in perspective, a human blink takes 100 to 150 milliseconds.<\/p>\n\n\n\n

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For systems with less than 10,000 products<\/strong> and a simple configuration<\/strong>, the response time is an impressive 6 to 7 milliseconds<\/strong> (ms). That’s just 1\/21 of the time it takes to blink an eye.<\/p>\n<\/div>

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When we scaled up the product data set to 100,000 products<\/strong> and activated more advanced features<\/strong>, we saw response times of 20 ms<\/strong> or about 1\/6 of a blink.<\/p>\n<\/div><\/div>\n\n\n\n

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We kept increasing the data size to around 750,000 products<\/strong> (a large dataset typical of B2B) along with complex features<\/strong>. FactFinder still delivers highly relevant results in just 50 to 60 ms<\/strong> or 2\/5 of a blink.<\/p>\n<\/div>

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Even with a massive catalog and multiple features, Factfinder\u2019s \u201cslowest\u201d response time leaves over 900 ms to build the page and deliver a sub-second response to the shopper.  <\/p>\n\n\n\n

That is true performance.<\/p>\n\n\n\n

Is your current search solution holding you back? <\/h2>\n\n\n\n

Discover how FactFinder can enhance your online store’s performance. Contact us today to see the difference.<\/a><\/p>\n\n\n\n

High speed does not always equal high performance<\/h2>\n\n\n\n

One mistake decision-makers continue making is thinking that high performance solely equals speed. While closely related, they refer to different aspects of search.  <\/p>\n\n\n\n